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 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer learning / fine-tuning\n",
    "\n",
    "This tutorial will guide you through the process of using _transfer learning_ to learn an accurate image classifier from a relatively small number of training samples. Generally speaking, transfer learning refers to the process of leveraging the knowledge learned in one model for the training of another model. \n",
    "\n",
    "More specifically, the process involves taking an existing neural network which was previously trained to good performance on a larger dataset, and using it as the basis for a new model which leverages that previous network's accuracy for a new task. This method has become popular in recent years to improve the performance of a neural net trained on a small dataset; the intuition is that the new dataset may be too small to train to good performance by itself, but we know that most neural nets trained to learn image features often learn similar features anyway, especially at early layers where they are more generic (edge detectors, blobs, and so on). \n",
    "\n",
    "Transfer learning has been largely enabled by the open-sourcing of state-of-the-art models; for the top performing models in image classification tasks (like from [ILSVRC](http://www.image-net.org/challenges/LSVRC/)), it is common practice now to not only publish the architecture, but to release the trained weights of the model as well. This lets amateurs use these top image classifiers to boost the performance of their own task-specific models.\n",
    "\n",
    "#### Feature extraction vs. fine-tuning\n",
    "\n",
    "At one extreme, transfer learning can involve taking the pre-trained network and freezing the weights, and using one of its hidden layers (usually the last one) as a feature extractor, using those features as the input to a smaller neural net. \n",
    "\n",
    "At the other extreme, we start with the pre-trained network, but don't freeze its weights, allowing them to be updated along with the new network. Another name for this procedure is called \"fine-tuning\" because we are slightly adjusting the pre-trained net's weights to the new task. We usually train such a network with a lower learning rate, since we expect the features are already relatively good and do not need to be changed too much. \n",
    "\n",
    "Sometimes, we do something in-between. Freeze just the early/generic layers, but fine-tune the later layers. Which strategy is best depends on the size of your dataset, the number of classes, and how much it resembles the dataset the previous model was trained on (and thus, whether it can benefit from the same learned feature extractors). A more detailed discussion of how to strategize can be found in [[1]](http://cs231n.github.io/transfer-learning/) [[2]](http://sebastianruder.com/transfer-learning/).\n",
    "\n",
    "## Procedure\n",
    "\n",
    "In this guide will go through the process of loading a state-of-the-art, 1000-class image classifier, [VGG16](https://arxiv.org/pdf/1409.1556.pdf) which [won the ImageNet challenge in 2014](http://www.robots.ox.ac.uk/~vgg/research/very_deep/), and using it as a fixed feature extractor to train a smaller custom classifier on our own images, although with very few code changes, you can try fine-tuning as well.\n",
    "\n",
    "We will first load VGG16 and remove its final layer, the 1000-class softmax classification layer specific to ImageNet, and replace it with a new classification layer for the classes we are training over. We will then freeze all the weights in the network except the new ones connecting to the new classification layer, and then train the new classification layer over our new dataset. \n",
    "\n",
    "We will also compare this method to training a small neural network from scratch on the new dataset, and as we shall see, it will dramatically improve our accuracy. We will do that part first.\n",
    "\n",
    "As our test subject, we'll use a dataset consisting of around 6000 images belonging to 97 classes, and train an image classifier with around 80% accuracy on it. It's worth noting that this strategy scales well to image sets where you may have even just a couple hundred or less images. Its performance will be lesser from a small number of samples (depending on classes) as usual, but still impressive considering the usual constraints.\n",
    "\n",
    "#### Implementation details\n",
    "\n",
    "This guide requires you to install [keras](http://www.keras.io), if you have not done so already. It is highly recommended to make sure you are using the GPU to train these models, as it will otherwise take much longer to train (however it is still possible to use CPU). If you can use GPU and have Theano as your backend, you should run the following command _before_ importing keras, to ensure it uses the GPU.\n",
    "\n",
    "    os.environ[\"THEANO_FLAGS\"] = \"mode=FAST_RUN,device=gpu,floatX=float32\"\n",
    "    \n",
    "If you are using Tensorflow as the backend, this is unnecessary. \n",
    "\n",
    "Note this guide uses quite deep networks, much larger than the ones we trained in the [convnets guide](https://github.com/ml4a/ml4a-guides/blob/master/notebooks/convolutional_neural_networks.ipynb). If your system does not have enough memory, you may experience out-of-memory errors running this guide. A duplicate of this guide using smaller networks and smaller images is forthcoming soon -- in the meantime, you can try changing the architecture to suit your memory constraints.\n",
    "\n",
    "To start, make sure the following import statements all work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "\n",
    "#if using Theano with GPU\n",
    "#os.environ[\"THEANO_FLAGS\"] = \"mode=FAST_RUN,device=gpu,floatX=float32\"\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting a dataset\n",
    "\n",
    "The first step is going to be to load our data. As our example, we will be using the dataset [CalTech-101](http://www.vision.caltech.edu/Image_Datasets/Caltech101/), which contains around 9000 labeled images belonging to 101 object categories. However, we will exclude 5 of the categories which have the most images. This is in order to keep the class distribution fairly balanced (around 50-100) and constrained to a smaller number of images, around 6000. \n",
    "\n",
    "To obtain this dataset, you can either run the download script `download.sh` in the `data` folder, or the following commands:\n",
    "\n",
    "    wget http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz\n",
    "    tar -xvzf 101_ObjectCategories.tar.gz\n",
    "\n",
    "If you wish to use your own dataset, it should be aranged in the same fashion to `101_ObjectCategories` with all of the images organized into subfolders, one for each class. In this case, the following cell should load your custom dataset correctly by just replacing `root` with your folder. If you have an alternate structure, you just need to make sure that you load the list `data` where every element is a dict where `x` is the data (a 1-d numpy array) and `y` is the label (an integer). Use the helper function `get_image(path)` to load the image correctly into the array, and note also that the images are being resized to 224x224. This is necessary because the input to VGG16 is a 224x224 RGB image. You do not need to resize them on your hard drive, as that is being done in the code below.\n",
    "\n",
    "If you have `101_ObjectCategories` in your data folder, the following cell should load all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished loading 6209 images from 97 categories\n",
      "train / validation / test split: 4346, 931, 932\n",
      "('training data shape: ', (4346, 224, 224, 3))\n",
      "('training labels shape: ', (4346, 97))\n"
     ]
    }
   ],
   "source": [
    "root = '../data/101_ObjectCategories'\n",
    "exclude = ['BACKGROUND_Google', 'Motorbikes', 'airplanes', 'Faces_easy', 'Faces']\n",
    "train_split, val_split = 0.7, 0.15\n",
    "\n",
    "categories = [x[0] for x in os.walk(root) if x[0]][1:]\n",
    "categories = [c for c in categories \n",
    "              if c not in [os.path.join(root, e) for e in exclude]]\n",
    "\n",
    "# helper function to load image and return it and input vector\n",
    "def get_image(path):\n",
    "    img = image.load_img(path, target_size=(224, 224))\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    return img, x\n",
    "\n",
    "# load all the images from root folder\n",
    "data = []\n",
    "for c, category in enumerate(categories):\n",
    "    images = [os.path.join(dp, f) for dp, dn, filenames \n",
    "              in os.walk(category) for f in filenames \n",
    "              if os.path.splitext(f)[1].lower() in ['.jpg','.png','.jpeg']]\n",
    "    for img_path in images:\n",
    "        img, x = get_image(img_path)\n",
    "        data.append({'x':np.array(x[0]), 'y':c})\n",
    "\n",
    "# count the number of classes\n",
    "num_classes = len(categories)\n",
    "\n",
    "# randomize the data order\n",
    "random.shuffle(data)\n",
    "\n",
    "# create training / validation / test split (70%, 15%, 15%)\n",
    "idx_val = int(train_split * len(data))\n",
    "idx_test = int((train_split + val_split) * len(data))\n",
    "train = data[:idx_val]\n",
    "val = data[idx_val:idx_test]\n",
    "test = data[idx_test:]\n",
    "\n",
    "# separate data for labels\n",
    "x_train, y_train = np.array([t[\"x\"] for t in train]), [t[\"y\"] for t in train]\n",
    "x_val, y_val = np.array([t[\"x\"] for t in val]), [t[\"y\"] for t in val]\n",
    "x_test, y_test = np.array([t[\"x\"] for t in test]), [t[\"y\"] for t in test]\n",
    "\n",
    "# normalize data\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_val = x_val.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "\n",
    "# convert labels to one-hot vectors\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_val = keras.utils.to_categorical(y_val, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "# summary\n",
    "print(\"finished loading %d images from %d categories\"%(len(data), num_classes))\n",
    "print(\"train / validation / test split: %d, %d, %d\"%(len(x_train), len(x_val), len(x_test)))\n",
    "print(\"training data shape: \", x_train.shape)\n",
    "print(\"training labels shape: \", y_train.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything worked properly, you should have loaded a bunch of images, and split them into three sets: `train`, `val`, and `test`. The shape of the training data should be (`n`, 224, 224, 3) where `n` is the size of your training set, and the labels should be (`n`, `c`) where `c` is the number of classes (97 in the case of `101_ObjectCategories`. \n",
    "\n",
    "Notice that we divided all the data into three subsets -- a training set `train`, a validation set `val`, and a test set `test`. The reason for this is to properly evaluate the accuracy of our classifier. During training, the optimizer uses the validation set to evaluate its internal performance, in order to determine the gradient without overfitting to the training set. The `test` set is always held out from the training algorithm, and is only used at the end to evaluate the final accuracy of our model.\n",
    "\n",
    "Let's quickly look at a few sample images from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f36ed790>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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smB82VE3HaGdGPsjJpSXzAR0j77/1XRY2cv2557lx53liN0XWU9544w1+8IN3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7AAAg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pXGGv1BH9txcByXxZVFU2AIsGwHPyhRkjW6S32qlRqNVoegMsLY1BzaLuF4ZdrdFo6tyJI+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\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+WlYgIZAlGV0RRLGEqlrEIi1mE6EBGrPnl1hdmWN6apDnXXk5SRgiqxaKqrNSqTO9az8n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szq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\nIiIiIjJQSkRFRERERERkoP4LL34bRvO5+EgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f2f99650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = [os.path.join(dp, f) for dp, dn, filenames in os.walk(root) for f in filenames if os.path.splitext(f)[1].lower() in ['.jpg','.png','.jpeg']]\n",
    "idx = [int(len(images) * random.random()) for i in range(8)]\n",
    "imgs = [image.load_img(images[i], target_size=(224, 224)) for i in idx]\n",
    "concat_image = np.concatenate([np.asarray(img) for img in imgs], axis=1)\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.imshow(concat_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First training a neural net from scratch\n",
    "\n",
    "Before doing the transfer learning, let's first build a neural network from scratch for doing classification on our dataset. This will give us a baseline to compare to our transfer-learned network later.\n",
    "\n",
    "The network we will construct contains 4 alternating convolutional and max-pooling layers, followed by a [dropout](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) after every other conv/pooling pair. After the last pooling layer, we will attach a fully-connected layer with 256 neurons, another dropout layer, then finally a softmax classification layer for our classes.\n",
    "\n",
    "Our loss function will be, as usual, categorical cross-entropy loss, and our learning algorithm will be [AdaDelta](https://arxiv.org/abs/1212.5701). Various things about this network can be changed to get better performance, perhaps using a larger network or a different optimizer will help, but for the purposes of this notebook, the goal is to just get an understanding of an approximate baseline for comparison's sake, and so it isn't neccessary to spend much time trying to optimize this network.\n",
    "\n",
    "Upon compiling the network, let's run `model.summary()` to get a snapshot of its layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_9 (Conv2D)            (None, 222, 222, 32)      896       \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 222, 222, 32)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 111, 111, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 109, 109, 32)      9248      \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 109, 109, 32)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 54, 54, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 54, 54, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 52, 52, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 52, 52, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 26, 26, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 24, 24, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 24, 24, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 256)               1179904   \n",
      "_________________________________________________________________\n",
      "activation_17 (Activation)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 97)                24929     \n",
      "_________________________________________________________________\n",
      "activation_18 (Activation)   (None, 97)                0         \n",
      "=================================================================\n",
      "Total params: 1,233,473.0\n",
      "Trainable params: 1,233,473.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# build the network\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), input_shape=x_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "# compile the model to use categorical cross-entropy loss function and adadelta optimizer\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adadelta',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've created a medium-sized network with ~1.2 million weights and biases (the parameters). Most of them are leading into the one pre-softmax fully-connected layer \"dense_5\".\n",
    "\n",
    "We can now go ahead and train our model for 100 epochs with a batch size of 128. We'll also record its history so we can plot the loss over time later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 4346 samples, validate on 931 samples\n",
      "Epoch 1/100\n",
      "4346/4346 [==============================] - 38s - loss: 4.5407 - acc: 0.0308 - val_loss: 4.4815 - val_acc: 0.0602\n",
      "Epoch 2/100\n",
      "4346/4346 [==============================] - 38s - loss: 4.4150 - acc: 0.0686 - val_loss: 4.3017 - val_acc: 0.0945\n",
      "Epoch 3/100\n",
      "4346/4346 [==============================] - 38s - loss: 4.2222 - acc: 0.1086 - val_loss: 4.0468 - val_acc: 0.1557\n",
      "Epoch 4/100\n",
      "4346/4346 [==============================] - 38s - loss: 3.9835 - acc: 0.1484 - val_loss: 3.7900 - val_acc: 0.1976\n",
      "Epoch 5/100\n",
      "4346/4346 [==============================] - 38s - loss: 3.7314 - acc: 0.1855 - val_loss: 3.6591 - val_acc: 0.2578\n",
      "Epoch 6/100\n",
      "4346/4346 [==============================] - 38s - loss: 3.5076 - acc: 0.2209 - val_loss: 3.4835 - val_acc: 0.2460\n",
      "Epoch 7/100\n",
      "4346/4346 [==============================] - 38s - loss: 3.2847 - acc: 0.2630 - val_loss: 3.2689 - val_acc: 0.2997\n",
      "Epoch 8/100\n",
      "4346/4346 [==============================] - 38s - loss: 3.0013 - acc: 0.3118 - val_loss: 3.1930 - val_acc: 0.3244\n",
      "Epoch 9/100\n",
      "4346/4346 [==============================] - 38s - loss: 2.7695 - acc: 0.3583 - val_loss: 2.9774 - val_acc: 0.3416\n",
      "Epoch 10/100\n",
      "4346/4346 [==============================] - 38s - loss: 2.5365 - acc: 0.3983 - val_loss: 2.8194 - val_acc: 0.3695\n",
      "Epoch 11/100\n",
      "4346/4346 [==============================] - 38s - loss: 2.3103 - acc: 0.4459 - val_loss: 2.7380 - val_acc: 0.3813\n",
      "Epoch 12/100\n",
      "4346/4346 [==============================] - 38s - loss: 2.1475 - acc: 0.4795 - val_loss: 2.6909 - val_acc: 0.3953\n",
      "Epoch 13/100\n",
      "4346/4346 [==============================] - 38s - loss: 1.8565 - acc: 0.5377 - val_loss: 2.7421 - val_acc: 0.3921\n",
      "Epoch 14/100\n",
      "4346/4346 [==============================] - 38s - loss: 1.6818 - acc: 0.5658 - val_loss: 2.5981 - val_acc: 0.4393\n",
      "Epoch 15/100\n",
      "4346/4346 [==============================] - 38s - loss: 1.5412 - acc: 0.6045 - val_loss: 2.5592 - val_acc: 0.4382\n",
      "Epoch 16/100\n",
      "4346/4346 [==============================] - 38s - loss: 1.3512 - acc: 0.6401 - val_loss: 2.6747 - val_acc: 0.4339\n",
      "Epoch 17/100\n",
      "4346/4346 [==============================] - 38s - loss: 1.2114 - acc: 0.6723 - val_loss: 2.6503 - val_acc: 0.4318\n",
      "Epoch 18/100\n",
      "4346/4346 [==============================] - 38s - loss: 1.0976 - acc: 0.7018 - val_loss: 2.6322 - val_acc: 0.4404\n",
      "Epoch 19/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.9964 - acc: 0.7260 - val_loss: 2.6771 - val_acc: 0.4479\n",
      "Epoch 20/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.9172 - acc: 0.7483 - val_loss: 2.6750 - val_acc: 0.4468\n",
      "Epoch 21/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.8427 - acc: 0.7655 - val_loss: 2.6560 - val_acc: 0.4726\n",
      "Epoch 22/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.7785 - acc: 0.7754 - val_loss: 2.7327 - val_acc: 0.4576\n",
      "Epoch 23/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.7053 - acc: 0.7977 - val_loss: 2.8198 - val_acc: 0.4533\n",
      "Epoch 24/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.6925 - acc: 0.7994 - val_loss: 2.7577 - val_acc: 0.4597\n",
      "Epoch 25/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.6092 - acc: 0.8182 - val_loss: 2.8431 - val_acc: 0.4468\n",
      "Epoch 26/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.5661 - acc: 0.8378 - val_loss: 2.9029 - val_acc: 0.4576\n",
      "Epoch 27/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.5162 - acc: 0.8532 - val_loss: 2.9416 - val_acc: 0.4576\n",
      "Epoch 28/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.5112 - acc: 0.8458 - val_loss: 2.9678 - val_acc: 0.4586\n",
      "Epoch 29/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.4802 - acc: 0.8555 - val_loss: 2.9655 - val_acc: 0.4694\n",
      "Epoch 30/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.4321 - acc: 0.8714 - val_loss: 2.9573 - val_acc: 0.4683\n",
      "Epoch 31/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.4379 - acc: 0.8711 - val_loss: 3.0498 - val_acc: 0.4705\n",
      "Epoch 32/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.3906 - acc: 0.8840 - val_loss: 3.0571 - val_acc: 0.4629\n",
      "Epoch 33/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.3787 - acc: 0.8898 - val_loss: 3.0878 - val_acc: 0.4565\n",
      "Epoch 34/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.3744 - acc: 0.8863 - val_loss: 3.0943 - val_acc: 0.4748\n",
      "Epoch 35/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.3785 - acc: 0.8896 - val_loss: 3.0990 - val_acc: 0.4726\n",
      "Epoch 36/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.3520 - acc: 0.8951 - val_loss: 3.1261 - val_acc: 0.4608\n",
      "Epoch 37/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.3447 - acc: 0.8990 - val_loss: 3.0691 - val_acc: 0.4629\n",
      "Epoch 38/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.3292 - acc: 0.8992 - val_loss: 3.1341 - val_acc: 0.4672\n",
      "Epoch 39/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2945 - acc: 0.9144 - val_loss: 3.1678 - val_acc: 0.4651\n",
      "Epoch 40/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2834 - acc: 0.9096 - val_loss: 3.2061 - val_acc: 0.4662\n",
      "Epoch 41/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2829 - acc: 0.9160 - val_loss: 3.2115 - val_acc: 0.4769\n",
      "Epoch 42/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2608 - acc: 0.9241 - val_loss: 3.2818 - val_acc: 0.4705\n",
      "Epoch 43/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2643 - acc: 0.9213 - val_loss: 3.2121 - val_acc: 0.4726\n",
      "Epoch 44/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2493 - acc: 0.9225 - val_loss: 3.2950 - val_acc: 0.4651\n",
      "Epoch 45/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2611 - acc: 0.9236 - val_loss: 3.3133 - val_acc: 0.4715\n",
      "Epoch 46/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2246 - acc: 0.9301 - val_loss: 3.2911 - val_acc: 0.4694\n",
      "Epoch 47/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2414 - acc: 0.9268 - val_loss: 3.2376 - val_acc: 0.4769\n",
      "Epoch 48/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2282 - acc: 0.9340 - val_loss: 3.2864 - val_acc: 0.4780\n",
      "Epoch 49/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2262 - acc: 0.9296 - val_loss: 3.3845 - val_acc: 0.4758\n",
      "Epoch 50/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2009 - acc: 0.9406 - val_loss: 3.3347 - val_acc: 0.4769\n",
      "Epoch 51/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1928 - acc: 0.9409 - val_loss: 3.3904 - val_acc: 0.4855\n",
      "Epoch 52/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1862 - acc: 0.9443 - val_loss: 3.4426 - val_acc: 0.4812\n",
      "Epoch 53/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1973 - acc: 0.9402 - val_loss: 3.4805 - val_acc: 0.4705\n",
      "Epoch 54/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2090 - acc: 0.9390 - val_loss: 3.3708 - val_acc: 0.4780\n",
      "Epoch 55/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1750 - acc: 0.9443 - val_loss: 3.4272 - val_acc: 0.4694\n",
      "Epoch 56/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.2037 - acc: 0.9409 - val_loss: 3.4955 - val_acc: 0.4823\n",
      "Epoch 57/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1705 - acc: 0.9501 - val_loss: 3.5468 - val_acc: 0.4919\n",
      "Epoch 58/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1724 - acc: 0.9494 - val_loss: 3.4323 - val_acc: 0.4672\n",
      "Epoch 59/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1681 - acc: 0.9503 - val_loss: 3.5111 - val_acc: 0.4866\n",
      "Epoch 60/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1963 - acc: 0.9399 - val_loss: 3.4187 - val_acc: 0.4791\n",
      "Epoch 61/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1667 - acc: 0.9514 - val_loss: 3.5126 - val_acc: 0.4823\n",
      "Epoch 62/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1443 - acc: 0.9597 - val_loss: 3.5175 - val_acc: 0.4866\n",
      "Epoch 63/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1520 - acc: 0.9549 - val_loss: 3.4818 - val_acc: 0.4780\n",
      "Epoch 64/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1390 - acc: 0.9584 - val_loss: 3.6060 - val_acc: 0.4715\n",
      "Epoch 65/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1446 - acc: 0.9521 - val_loss: 3.4628 - val_acc: 0.4758\n",
      "Epoch 66/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1314 - acc: 0.9586 - val_loss: 3.5667 - val_acc: 0.4737\n",
      "Epoch 67/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1355 - acc: 0.9572 - val_loss: 3.5765 - val_acc: 0.4769\n",
      "Epoch 68/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1346 - acc: 0.9579 - val_loss: 3.5701 - val_acc: 0.4715\n",
      "Epoch 69/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1451 - acc: 0.9544 - val_loss: 3.5311 - val_acc: 0.4737\n",
      "Epoch 70/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1497 - acc: 0.9572 - val_loss: 3.6496 - val_acc: 0.4715\n",
      "Epoch 71/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1320 - acc: 0.9611 - val_loss: 3.5732 - val_acc: 0.4694\n",
      "Epoch 72/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1138 - acc: 0.9664 - val_loss: 3.6330 - val_acc: 0.4737\n",
      "Epoch 73/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1384 - acc: 0.9588 - val_loss: 3.6614 - val_acc: 0.4737\n",
      "Epoch 74/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1334 - acc: 0.9607 - val_loss: 3.6358 - val_acc: 0.4672\n",
      "Epoch 75/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1167 - acc: 0.9666 - val_loss: 3.6078 - val_acc: 0.4758\n",
      "Epoch 76/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1354 - acc: 0.9639 - val_loss: 3.6409 - val_acc: 0.4758\n",
      "Epoch 77/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1202 - acc: 0.9627 - val_loss: 3.5874 - val_acc: 0.4834\n",
      "Epoch 78/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1076 - acc: 0.9680 - val_loss: 3.7173 - val_acc: 0.4694\n",
      "Epoch 79/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1389 - acc: 0.9565 - val_loss: 3.5808 - val_acc: 0.4801\n",
      "Epoch 80/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1281 - acc: 0.9648 - val_loss: 3.6159 - val_acc: 0.4780\n",
      "Epoch 81/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1049 - acc: 0.9678 - val_loss: 3.6340 - val_acc: 0.4726\n",
      "Epoch 82/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1013 - acc: 0.9676 - val_loss: 3.6720 - val_acc: 0.4801\n",
      "Epoch 83/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0965 - acc: 0.9731 - val_loss: 3.7164 - val_acc: 0.4844\n",
      "Epoch 84/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0983 - acc: 0.9715 - val_loss: 3.7099 - val_acc: 0.4844\n",
      "Epoch 85/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1251 - acc: 0.9611 - val_loss: 3.6103 - val_acc: 0.4662\n",
      "Epoch 86/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0977 - acc: 0.9673 - val_loss: 3.5529 - val_acc: 0.4823\n",
      "Epoch 87/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0997 - acc: 0.9685 - val_loss: 3.7150 - val_acc: 0.4672\n",
      "Epoch 88/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0945 - acc: 0.9685 - val_loss: 3.8007 - val_acc: 0.4769\n",
      "Epoch 89/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1063 - acc: 0.9664 - val_loss: 3.8063 - val_acc: 0.4748\n",
      "Epoch 90/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1029 - acc: 0.9694 - val_loss: 3.7229 - val_acc: 0.4866\n",
      "Epoch 91/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1127 - acc: 0.9666 - val_loss: 3.6706 - val_acc: 0.4855\n",
      "Epoch 92/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0993 - acc: 0.9694 - val_loss: 3.6673 - val_acc: 0.4941\n",
      "Epoch 93/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1001 - acc: 0.9678 - val_loss: 3.7259 - val_acc: 0.4726\n",
      "Epoch 94/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0922 - acc: 0.9731 - val_loss: 3.6871 - val_acc: 0.4748\n",
      "Epoch 95/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0786 - acc: 0.9761 - val_loss: 3.7414 - val_acc: 0.4651\n",
      "Epoch 96/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1023 - acc: 0.9682 - val_loss: 3.7787 - val_acc: 0.4844\n",
      "Epoch 97/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0895 - acc: 0.9747 - val_loss: 3.7143 - val_acc: 0.4876\n",
      "Epoch 98/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.1098 - acc: 0.9685 - val_loss: 3.6360 - val_acc: 0.4791\n",
      "Epoch 99/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0858 - acc: 0.9731 - val_loss: 3.7660 - val_acc: 0.4844\n",
      "Epoch 100/100\n",
      "4346/4346 [==============================] - 38s - loss: 0.0875 - acc: 0.9733 - val_loss: 3.6681 - val_acc: 0.4855\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=128,\n",
    "                    epochs=100,\n",
    "                    validation_data=(x_val, y_val))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Let's plot the validation loss and validation accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nn2DIEE+g3nhj/e323BNuuQWOOabkfV11lSed77/vhYaef95bZ4cNq6zoq48l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\nNBZsUCVJkiRJY8EGVZIkSZI0Fv4P5cbCOVYkveEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f2c51d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,4))\n",
    "ax = fig.add_subplot(121)\n",
    "ax.plot(history.history[\"val_loss\"])\n",
    "ax.set_title(\"validation loss\")\n",
    "ax.set_xlabel(\"epochs\")\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(history.history[\"val_acc\"])\n",
    "ax2.set_title(\"validation accuracy\")\n",
    "ax2.set_xlabel(\"epochs\")\n",
    "ax2.set_ylim(0, 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the validation loss begins to actually rise after around 16 epochs, even though validation accuracy remains roughly between 40% and 50%. This suggests our model begins overfitting around then, and best performance would have been achieved if we had stopped early around then. Nevertheless, our accuracy would not have likely been above 50%, and probably lower down.\n",
    "\n",
    "We can also get a final evaluation by running our model on the training set. Doing so, we get the following results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Test loss:', 3.7721598874857496)\n",
      "('Test accuracy:', 0.49463519313304721)\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', loss)\n",
    "print('Test accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we see that we have achieved a (top-1) accuracy of around 49%. That's not too bad for 6000 images, considering that if we were to use a naive strategy of taking random guesses, we would have only gotten around 1% accuracy. \n",
    "\n",
    "## Transfer learning by starting with existing network\n",
    "\n",
    "Now we can move on to the main strategy for training an image classifier on our small dataset: by starting with a larger and already trained network.\n",
    "\n",
    "To start, we will load the VGG16 from keras, which was trained on ImageNet and the weights saved online. If this is your first time loading VGG16, you'll need to wait a bit for the weights to download from the web. Once the network is loaded, we can again inspect the layers with the `summary()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544.0\n",
      "Trainable params: 138,357,544.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg = keras.applications.VGG16(weights='imagenet', include_top=True)\n",
    "vgg.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Notice that VGG16 is _much_ bigger than the network we constructed earlier. It contains 13 convolutional layers and two fully connected layers at the end, and has over 138 million parameters, around 100 times as many parameters than the network we made above. Like our first network, the majority of the parameters are stored in the connections leading into the first fully-connected layer.\n",
    "\n",
    "VGG16 was made to solve ImageNet, and achieves a [8.8% top-5 error rate](https://github.com/jcjohnson/cnn-benchmarks), which means that 91.2% of test samples were classified correctly within the top 5 predictions for each image. It's top-1 accuracy--equivalent to the accuracy metric we've been using (that the top prediction is correct)--is 73%. This is especially impressive since there are not just 97, but 1000 classes, meaning that random guesses would get us only 0.1% accuracy.\n",
    "\n",
    "In order to use this network for our task, we \"remove\" the final classification layer, the 1000-neuron softmax layer at the end, which corresponds to ImageNet, and instead replace it with a new softmax layer for our dataset, which contains 97 neurons in the case of the 101_ObjectCategories dataset. \n",
    "\n",
    "In terms of implementation, it's easier to simply create a copy of VGG from its input layer until the second to last layer, and then work with that, rather than modifying the VGG object directly. So technically we never \"remove\" anything, we just circumvent/ignore it. This can be done in the following way, by using the keras `Model` class to initialize a new model whose input layer is the same as VGG but whose output layer is our new softmax layer, called `new_classification_layer`. Note: although it appears we are duplicating this large network, internally Keras is actually just copying all the layers by reference, and thus we don't need to worry about overloading the memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# make a reference to VGG's input layer\n",
    "inp = vgg.input\n",
    "\n",
    "# make a new softmax layer with num_classes neurons\n",
    "new_classification_layer = Dense(num_classes, activation='softmax')\n",
    "\n",
    "# connect our new layer to the second to last layer in VGG, and make a reference to it\n",
    "out = new_classification_layer(vgg.layers[-2].output)\n",
    "\n",
    "# create a new network between inp and out\n",
    "model_new = Model(inp, out)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to retrain this network, `model_new` on the new dataset and labels. But first, we need to freeze the weights and biases in all the layers in the network, except our new one at the end, with the expectation that the features that were learned in VGG should still be fairly relevant to the new image classification task. Not optimal, but most likely better than what we can train to in our limited dataset. \n",
    "\n",
    "By setting the `trainable` flag in each layer false (except our new classification layer), we ensure all the weights and biases in those layers remain fixed, and we simply train the weights in the one layer at the end. In some cases, it is desirable to *not* freeze all the pre-classification layers. If your dataset has enough samples, and doesn't resemble ImageNet very much, it might be advantageous to fine-tune some of the VGG layers along with the new classifier, or possibly even all of them. To do this, you can change the below code to make more of the layers trainable.\n",
    "\n",
    "In the case of CalTech-101, we will just do feature extraction, fearing that fine-tuning too much with this dataset may overfit. But maybe we are wrong? A good exercise would be to try out both, and compare the results.\n",
    "\n",
    "So we go ahead and freeze the layers, and compile the new model with exactly the same optimizer and loss function as in our first network, for the sake of a fair comparison. We then run `summary` again to look at the network's architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 97)                397409    \n",
      "=================================================================\n",
      "Total params: 134,657,953.0\n",
      "Trainable params: 397,409.0\n",
      "Non-trainable params: 134,260,544.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# make all layers untrainable by freezing weights (except for last layer)\n",
    "for l, layer in enumerate(model_new.layers[:-1]):\n",
    "    layer.trainable = False\n",
    "\n",
    "# ensure the last layer is trainable/not frozen\n",
    "for l, layer in enumerate(model_new.layers[-1:]):\n",
    "    layer.trainable = True\n",
    "\n",
    "model_new.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adadelta',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model_new.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the summary, we see the network is identical to the VGG model we instantiated earlier, except the last layer, formerly a 1000-neuron softmax, has been replaced by a new 97-neuron softmax. Additionally, we still have roughly 134 million weights, but now the vast majority of them are \"non-trainable params\" because we froze the layers they are contained in. We now only have 397,000 trainable parameters, which is actually only a quarter of the number of parameters needed to train the first model.\n",
    "\n",
    "As before, we go ahead and train the new model, using the same hyperparameters (batch size and number of epochs) as before, along with the same optimization algorithm. We also keep track of its history as we go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 4346 samples, validate on 931 samples\n",
      "Epoch 1/100\n",
      "4346/4346 [==============================] - 36s - loss: 4.2942 - acc: 0.0900 - val_loss: 3.7752 - val_acc: 0.1472\n",
      "Epoch 2/100\n",
      "4346/4346 [==============================] - 36s - loss: 3.4561 - acc: 0.2584 - val_loss: 3.2105 - val_acc: 0.3212\n",
      "Epoch 3/100\n",
      "4346/4346 [==============================] - 36s - loss: 2.9204 - acc: 0.3684 - val_loss: 2.7843 - val_acc: 0.3910\n",
      "Epoch 4/100\n",
      "4346/4346 [==============================] - 36s - loss: 2.5652 - acc: 0.4473 - val_loss: 2.5515 - val_acc: 0.4823\n",
      "Epoch 5/100\n",
      "4346/4346 [==============================] - 36s - loss: 2.2863 - acc: 0.5166 - val_loss: 2.2879 - val_acc: 0.4791\n",
      "Epoch 6/100\n",
      "4346/4346 [==============================] - 36s - loss: 2.0762 - acc: 0.5511 - val_loss: 2.1597 - val_acc: 0.5188\n",
      "Epoch 7/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.9124 - acc: 0.5874 - val_loss: 1.9776 - val_acc: 0.5585\n",
      "Epoch 8/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.7626 - acc: 0.6197 - val_loss: 1.9062 - val_acc: 0.5671\n",
      "Epoch 9/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.6470 - acc: 0.6523 - val_loss: 1.7965 - val_acc: 0.5994\n",
      "Epoch 10/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.5568 - acc: 0.6707 - val_loss: 1.6949 - val_acc: 0.6079\n",
      "Epoch 11/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.4616 - acc: 0.6901 - val_loss: 1.6447 - val_acc: 0.6047\n",
      "Epoch 12/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.3878 - acc: 0.7103 - val_loss: 1.5615 - val_acc: 0.6337\n",
      "Epoch 13/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.3259 - acc: 0.7112 - val_loss: 1.5190 - val_acc: 0.6488\n",
      "Epoch 14/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.2700 - acc: 0.7333 - val_loss: 1.4809 - val_acc: 0.6574\n",
      "Epoch 15/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.2156 - acc: 0.7430 - val_loss: 1.4060 - val_acc: 0.6874\n",
      "Epoch 16/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.1614 - acc: 0.7545 - val_loss: 1.3809 - val_acc: 0.6702\n",
      "Epoch 17/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.1213 - acc: 0.7549 - val_loss: 1.3757 - val_acc: 0.6735\n",
      "Epoch 18/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.0823 - acc: 0.7623 - val_loss: 1.3053 - val_acc: 0.6992\n",
      "Epoch 19/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.0498 - acc: 0.7743 - val_loss: 1.2646 - val_acc: 0.6917\n",
      "Epoch 20/100\n",
      "4346/4346 [==============================] - 36s - loss: 1.0119 - acc: 0.7780 - val_loss: 1.2462 - val_acc: 0.6982\n",
      "Epoch 21/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.9782 - acc: 0.7902 - val_loss: 1.2202 - val_acc: 0.7014\n",
      "Epoch 22/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.9491 - acc: 0.7936 - val_loss: 1.2190 - val_acc: 0.7025\n",
      "Epoch 23/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.9309 - acc: 0.7927 - val_loss: 1.2055 - val_acc: 0.7068\n",
      "Epoch 24/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.8900 - acc: 0.8125 - val_loss: 1.1590 - val_acc: 0.7186\n",
      "Epoch 25/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.8782 - acc: 0.8127 - val_loss: 1.1377 - val_acc: 0.7272\n",
      "Epoch 26/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.8557 - acc: 0.8097 - val_loss: 1.1375 - val_acc: 0.7057\n",
      "Epoch 27/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.8281 - acc: 0.8180 - val_loss: 1.1416 - val_acc: 0.7111\n",
      "Epoch 28/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.8066 - acc: 0.8233 - val_loss: 1.1032 - val_acc: 0.7218\n",
      "Epoch 29/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.7975 - acc: 0.8208 - val_loss: 1.0881 - val_acc: 0.7272\n",
      "Epoch 30/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.7724 - acc: 0.8302 - val_loss: 1.0782 - val_acc: 0.7304\n",
      "Epoch 31/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.7553 - acc: 0.8329 - val_loss: 1.0862 - val_acc: 0.7175\n",
      "Epoch 32/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.7379 - acc: 0.8382 - val_loss: 1.0506 - val_acc: 0.7390\n",
      "Epoch 33/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.7256 - acc: 0.8376 - val_loss: 1.0220 - val_acc: 0.7551\n",
      "Epoch 34/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.7063 - acc: 0.8433 - val_loss: 1.0208 - val_acc: 0.7379\n",
      "Epoch 35/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.6978 - acc: 0.8442 - val_loss: 0.9990 - val_acc: 0.7540\n",
      "Epoch 36/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.6751 - acc: 0.8530 - val_loss: 1.0387 - val_acc: 0.7272\n",
      "Epoch 37/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.6700 - acc: 0.8442 - val_loss: 0.9966 - val_acc: 0.7476\n",
      "Epoch 38/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.6557 - acc: 0.8573 - val_loss: 0.9849 - val_acc: 0.7594\n",
      "Epoch 39/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.6431 - acc: 0.8585 - val_loss: 0.9924 - val_acc: 0.7465\n",
      "Epoch 40/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.6315 - acc: 0.8679 - val_loss: 0.9706 - val_acc: 0.7487\n",
      "Epoch 41/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.6202 - acc: 0.8603 - val_loss: 0.9716 - val_acc: 0.7530\n",
      "Epoch 42/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.6090 - acc: 0.8677 - val_loss: 0.9680 - val_acc: 0.7583\n",
      "Epoch 43/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.5978 - acc: 0.8677 - val_loss: 0.9738 - val_acc: 0.7530\n",
      "Epoch 44/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5870 - acc: 0.8744 - val_loss: 0.9347 - val_acc: 0.7766\n",
      "Epoch 45/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.5753 - acc: 0.8771 - val_loss: 0.9095 - val_acc: 0.7701\n",
      "Epoch 46/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5675 - acc: 0.8792 - val_loss: 0.9525 - val_acc: 0.7573\n",
      "Epoch 47/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5587 - acc: 0.8753 - val_loss: 0.9459 - val_acc: 0.7476\n",
      "Epoch 48/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5555 - acc: 0.8794 - val_loss: 0.9261 - val_acc: 0.7530\n",
      "Epoch 49/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5383 - acc: 0.8836 - val_loss: 0.9454 - val_acc: 0.7454\n",
      "Epoch 50/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5342 - acc: 0.8840 - val_loss: 0.9138 - val_acc: 0.7615\n",
      "Epoch 51/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5291 - acc: 0.8870 - val_loss: 0.9070 - val_acc: 0.7573\n",
      "Epoch 52/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5148 - acc: 0.8861 - val_loss: 0.9018 - val_acc: 0.7680\n",
      "Epoch 53/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5096 - acc: 0.8886 - val_loss: 0.9226 - val_acc: 0.7530\n",
      "Epoch 54/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.5027 - acc: 0.8939 - val_loss: 0.8943 - val_acc: 0.7744\n",
      "Epoch 55/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4936 - acc: 0.8960 - val_loss: 0.9074 - val_acc: 0.7691\n",
      "Epoch 56/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4879 - acc: 0.8953 - val_loss: 0.9124 - val_acc: 0.7540\n",
      "Epoch 57/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4792 - acc: 0.8965 - val_loss: 0.8896 - val_acc: 0.7744\n",
      "Epoch 58/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4755 - acc: 0.8960 - val_loss: 0.8891 - val_acc: 0.7658\n",
      "Epoch 59/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4682 - acc: 0.9008 - val_loss: 0.8810 - val_acc: 0.7701\n",
      "Epoch 60/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4641 - acc: 0.8985 - val_loss: 0.8869 - val_acc: 0.7820\n",
      "Epoch 61/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4495 - acc: 0.9036 - val_loss: 0.8652 - val_acc: 0.7744\n",
      "Epoch 62/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.4485 - acc: 0.9040 - val_loss: 0.8858 - val_acc: 0.7680\n",
      "Epoch 63/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.4396 - acc: 0.9103 - val_loss: 0.8642 - val_acc: 0.7755\n",
      "Epoch 64/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.4323 - acc: 0.9116 - val_loss: 0.8702 - val_acc: 0.7798\n",
      "Epoch 65/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4302 - acc: 0.9059 - val_loss: 0.8904 - val_acc: 0.7594\n",
      "Epoch 66/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.4272 - acc: 0.9110 - val_loss: 0.8638 - val_acc: 0.7744\n",
      "Epoch 67/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4167 - acc: 0.9146 - val_loss: 0.8752 - val_acc: 0.7723\n",
      "Epoch 68/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.4100 - acc: 0.9183 - val_loss: 0.8653 - val_acc: 0.7787\n",
      "Epoch 69/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.4076 - acc: 0.9144 - val_loss: 0.8573 - val_acc: 0.7658\n",
      "Epoch 70/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.4026 - acc: 0.9162 - val_loss: 0.8291 - val_acc: 0.7798\n",
      "Epoch 71/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3964 - acc: 0.9185 - val_loss: 0.8721 - val_acc: 0.7701\n",
      "Epoch 72/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3944 - acc: 0.9156 - val_loss: 0.8509 - val_acc: 0.7787\n",
      "Epoch 73/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3897 - acc: 0.9197 - val_loss: 0.8458 - val_acc: 0.7744\n",
      "Epoch 74/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3819 - acc: 0.9206 - val_loss: 0.8388 - val_acc: 0.7852\n",
      "Epoch 75/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3787 - acc: 0.9215 - val_loss: 0.8281 - val_acc: 0.7691\n",
      "Epoch 76/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3757 - acc: 0.9213 - val_loss: 0.8352 - val_acc: 0.7701\n",
      "Epoch 77/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3713 - acc: 0.9287 - val_loss: 0.8344 - val_acc: 0.7766\n",
      "Epoch 78/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3664 - acc: 0.9243 - val_loss: 0.8259 - val_acc: 0.7830\n",
      "Epoch 79/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3614 - acc: 0.9273 - val_loss: 0.8162 - val_acc: 0.7830\n",
      "Epoch 80/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3563 - acc: 0.9271 - val_loss: 0.8301 - val_acc: 0.7734\n",
      "Epoch 81/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3527 - acc: 0.9261 - val_loss: 0.8223 - val_acc: 0.7863\n",
      "Epoch 82/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3499 - acc: 0.9294 - val_loss: 0.7862 - val_acc: 0.7927\n",
      "Epoch 83/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3399 - acc: 0.9342 - val_loss: 0.8135 - val_acc: 0.7766\n",
      "Epoch 84/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3410 - acc: 0.9337 - val_loss: 0.8146 - val_acc: 0.7830\n",
      "Epoch 85/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3354 - acc: 0.9335 - val_loss: 0.7957 - val_acc: 0.7905\n",
      "Epoch 86/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3355 - acc: 0.9314 - val_loss: 0.8126 - val_acc: 0.7820\n",
      "Epoch 87/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3307 - acc: 0.9344 - val_loss: 0.8042 - val_acc: 0.7820\n",
      "Epoch 88/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3240 - acc: 0.9386 - val_loss: 0.7895 - val_acc: 0.8002\n",
      "Epoch 89/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3210 - acc: 0.9388 - val_loss: 0.8042 - val_acc: 0.7927\n",
      "Epoch 90/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3167 - acc: 0.9365 - val_loss: 0.8036 - val_acc: 0.7809\n",
      "Epoch 91/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3138 - acc: 0.9416 - val_loss: 0.7923 - val_acc: 0.7863\n",
      "Epoch 92/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3126 - acc: 0.9390 - val_loss: 0.7948 - val_acc: 0.7916\n",
      "Epoch 93/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3083 - acc: 0.9422 - val_loss: 0.7973 - val_acc: 0.7895\n",
      "Epoch 94/100\n",
      "4346/4346 [==============================] - 37s - loss: 0.3057 - acc: 0.9422 - val_loss: 0.7880 - val_acc: 0.7916\n",
      "Epoch 95/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.3004 - acc: 0.9448 - val_loss: 0.8244 - val_acc: 0.7755\n",
      "Epoch 96/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.2986 - acc: 0.9439 - val_loss: 0.7961 - val_acc: 0.7884\n",
      "Epoch 97/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.2966 - acc: 0.9459 - val_loss: 0.7892 - val_acc: 0.7927\n",
      "Epoch 98/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.2932 - acc: 0.9439 - val_loss: 0.7866 - val_acc: 0.8002\n",
      "Epoch 99/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.2887 - acc: 0.9473 - val_loss: 0.7858 - val_acc: 0.7948\n",
      "Epoch 100/100\n",
      "4346/4346 [==============================] - 36s - loss: 0.2862 - acc: 0.9471 - val_loss: 0.7880 - val_acc: 0.7841\n"
     ]
    }
   ],
   "source": [
    "history2 = model_new.fit(x_train, y_train, \n",
    "                         batch_size=128, \n",
    "                         epochs=100, \n",
    "                         validation_data=(x_val, y_val))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our validation accuracy hovers close to 80% towards the end, which is more than 30% improvement on the original network trained from scratch (meaning that we make the wrong prediction on 20% of samples, rather than 50%). \n",
    "\n",
    "It's worth noting also that this network actually trains _slightly faster_ than the original network, despite having more than 100 times as many parameters! This is because freezing the weights negates the need to backpropagate through all those layers, saving us on runtime.\n",
    "\n",
    "Let's plot the validation loss and accuracy again, this time comparing the original model trained from scratch (in blue) and the new transfer-learned model in green."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HH8HevUl/LqWUUkqlXik2Qc2fH955BwYPdnUkSimVep09C61bww8/QIMGdgT1jrZt4YMP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\nUEVERERERKROUEAVERERERGROuH/AYuEKtDXSpJ9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f324f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,4))\n",
    "ax = fig.add_subplot(121)\n",
    "ax.plot(history.history[\"val_loss\"])\n",
    "ax.plot(history2.history[\"val_loss\"])\n",
    "ax.set_title(\"validation loss\")\n",
    "ax.set_xlabel(\"epochs\")\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(history.history[\"val_acc\"])\n",
    "ax2.plot(history2.history[\"val_acc\"])\n",
    "ax2.set_title(\"validation accuracy\")\n",
    "ax2.set_xlabel(\"epochs\")\n",
    "ax2.set_ylim(0, 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that whereas the original model began overfitting around epoch 16, the new model continued to slowly decrease its loss over time, and likely would have improved its accuracy slightly with more iterations. The new model made it to roughly 80% top-1 accuracy (in the validation set) and continued to improve slowly through 100 epochs.\n",
    "\n",
    "It's possibly we could have improved the original model with better regularization or more dropout, but we surely would not have made up the >30% improvement in accuracy. \n",
    "\n",
    "Again, we do a final validation on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Test loss:', 0.8078902146335324)\n",
      "('Test accuracy:', 0.7821888412017167)\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model_new.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "print('Test loss:', loss)\n",
    "print('Test accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict a new image, simply run the following code to get the probabilities for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img, x = get_image('../data/101_ObjectCategories/airplanes/image_0003.jpg')\n",
    "probabilities = model_new.predict([x])\n",
    "print(probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Improving the results\n",
    "\n",
    "78.2% top-1 accuracy on 97 classes, roughly evenly distributed, is a pretty good achievement. It is not quite as impressive as the original VGG16 which achieved 73% top-1 accuracy on 1000 classes. Nevertheless, it is much better than what we were able to achieve with our original network, and there is room for improvement. Some techniques which possibly could have improved our performance.\n",
    "\n",
    "- Using data augementation: augmentation refers to using various modifications of the original training data, in the form of distortions, rotations, rescalings, lighting changes, etc to increase the size of the training set and create more tolerance for such distortions.\n",
    "- Using a different optimizer, adding more regularization/dropout, and other hyperparameters.\n",
    "- Training for longer (of course)\n",
    "\n",
    "A more advanced example of transfer learning in Keras, involving augmentation for a small 2-class dataset, can be found in the [Keras blog](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)."
   ]
  }
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